Instructions to use google/gemma-2-2b-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/gemma-2-2b-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="google/gemma-2-2b-it") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-2b-it") model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use google/gemma-2-2b-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "google/gemma-2-2b-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/gemma-2-2b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/google/gemma-2-2b-it
- SGLang
How to use google/gemma-2-2b-it with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "google/gemma-2-2b-it" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/gemma-2-2b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "google/gemma-2-2b-it" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "google/gemma-2-2b-it", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use google/gemma-2-2b-it with Docker Model Runner:
docker model run hf.co/google/gemma-2-2b-it
Problem with 'google/gemma-2-2b-it''s API for Chat completion
Hi !
I am in front of a big problem, while it seems that the API google/gemma-2-2b-it (Official Hugging Face documentation for 'Chat Completion : curl 'https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/completions' \ -H "Authorization: Bearer hf_***" \ -H 'Content-Type: application/json' \ -d '{ "model": "google/gemma-2-2b-it", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 500, "stream": false }') is not working for "Chat Completion".
The address 'https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/completions' points to ```// 20240918223200
// https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/completions
{
"error": "Model google/gemma-2-2b-it/v1/chat/completions does not exist"
}```.
Which correct API could i use in order to call properly the google/gemma-2-2b-itChat completion please ?
Thx !
I was able to reproduce the issue. To resolve it, please use the following API endpoint: https://api-inference.huggingface.co/models/google/gemma-2-2b-it and refer to the corrected code below:
Thank you.
Thx @GopiUppari for your answer.
Yeah it works for me this way, anyway it appears that this solution reproduce a 'text-to-text' AI API call.
Unfortunately it doesn't reproduce a 'Chat completion', or a conversation with google/gemma-2-2b-it.
It doesn't accept the "messages": [ { "role": "user", "content": "What is the best approach for integrating AI and blockchain technologies in a decentralized application?" } ], option from { "model": "google/gemma-2-2b-it", "messages": [ { "role": "user", "content": "What is the best approach for integrating AI and blockchain technologies in a decentralized application?" } ], "max_tokens": 500, "temperature": 0.7, "top_p": 0.95, "repetition_penalty": 1.15, "stream": false } body-request pattern.
Indeed, the 'Chat completion' documentation says that curl 'https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/completions' \ -H "Authorization: Bearer hf_***" \ -H 'Content-Type: application/json' \ -d '{ "model": "google/gemma-2-2b-it", "messages": [{"role": "user", "content": "What is the capital of France?"}], "max_tokens": 500, "stream": false }should work, but it didn't due to https://api-inference.huggingface.co/models/google/gemma-2-2b-it/v1/chat/completions API which doesn't exist.
How can I use the conversationnal API call of google/gemma-2-2b-it please ?
Thx !
In the documentation, passing the chat template format to the tokenizer.apply_chat_template function returns a string format (<class 'str'>) that the model can interpret. You can use this same formatted string in the curl command to ensure the model understands the input correctly.
Thank you.


